{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:18:32Z","timestamp":1760149112534,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000192","name":"NOAA Cooperative Science Center","doi-asserted-by":"publisher","award":["NA22SEC4810016"],"award-info":[{"award-number":["NA22SEC4810016"]}],"id":[{"id":"10.13039\/100000192","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the \u201cunknown truth\u201d. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1\/8\u00b0, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions.<\/jats:p>","DOI":"10.3390\/rs15133450","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:47:35Z","timestamp":1688950055000},"page":"3450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4303-7500","authenticated-orcid":false,"given":"Zhen","family":"Hong","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0408-6588","authenticated-orcid":false,"given":"Hernan A.","family":"Moreno","sequence":"additional","affiliation":[{"name":"Department of Earth, Environmental and Resource Sciences, University of Texas, El Paso, TX 79902, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5047-5384","authenticated-orcid":false,"given":"Laura V.","family":"Alvarez","sequence":"additional","affiliation":[{"name":"Department of Earth, Environmental and Resource Sciences, University of Texas, El Paso, TX 79902, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4760-842X","authenticated-orcid":false,"given":"Zhi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA"}]},{"given":"Yang","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","unstructured":"Dingman, S.L. (2015). Physical Hydrology, Waveland Press. [3rd ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","article-title":"Investigating soil moisture\u2014Climate interactions in a changing climate: A review","volume":"99","author":"Seneviratne","year":"2010","journal-title":"Earth-Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.wace.2015.05.001","article-title":"Impact of soil moisture on extreme maximum temperatures in Europe","volume":"9","author":"Whan","year":"2015","journal-title":"Weather. Clim. Extremes"},{"key":"ref_4","first-page":"1","article-title":"On the role of soil moisture in the generation of heavy rainfall during the Oder flood event in July 1997","volume":"67","author":"Hagemann","year":"2015","journal-title":"Tellus A Dyn. Meteorol. Oceanogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"L18401","DOI":"10.1029\/2005GL023543","article-title":"The added value of spaceborne passive microwave soil moisture retrievals for forecasting rainfall-runoff partitioning","volume":"32","author":"Crow","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.5194\/hess-14-1881-2010","article-title":"Improving runoff prediction through the assimilation of the ASCAT soil moisture product","volume":"14","author":"Brocca","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mohd Kassim, M.R., Mat, I., and Harun, A.N. (2014, January 7\u20139). Wireless Sensor Network in Precision Agriculture Application. Proceedings of the 2014 International Conference on Computer, Information and Telecommunication Systems (CITS), Jeju Island, Republic of Korea.","DOI":"10.1109\/CITS.2014.6878963"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"L22401","DOI":"10.1029\/2008GL035772","article-title":"Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data","volume":"35","author":"Gu","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1175\/JHM-D-16-0045.1","article-title":"Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China","volume":"18","author":"Zhang","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.3390\/rs4051232","article-title":"Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy","volume":"4","author":"Brocca","year":"2012","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1016\/j.rse.2010.05.033","article-title":"Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US","volume":"114","author":"Ray","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_13","first-page":"W01423","article-title":"Field observations of soil moisture variability across scales","volume":"44","author":"Famiglietti","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"RG2002","DOI":"10.1029\/2011RG000372","article-title":"Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products","volume":"50","author":"Crow","year":"2012","journal-title":"Rev. Geophys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1029\/2018WR024535","article-title":"Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging","volume":"55","author":"Ochsner","year":"2019","journal-title":"Water Resour. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.07.003","article-title":"Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia","volume":"138","author":"Qin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11372","DOI":"10.3390\/rs70911372","article-title":"Upscaling In Situ Soil Moisture Observations to Pixel Averages with Spatio-Temporal Geostatistics","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, J., Jin, R., Li, X., and Zhang, Y. (2021). Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products. Remote Sens., 13.","DOI":"10.3390\/rs13020228"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hong, Z., Moreno, H.A., Li, Z., Li, S., Greene, J.S., Hong, Y., and Alvarez, L.V. (2022). Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma\u2014Part I: Individual Product Assessment. Remote Sens., 14.","DOI":"10.3390\/rs14225641"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"W11502","DOI":"10.1029\/2011WR011682","article-title":"An objective methodology for merging satellite- and model-based soil moisture products","volume":"48","author":"Yilmaz","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1175\/JHM-D-17-0063.1","article-title":"Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements","volume":"18","author":"Reichle","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1175\/JHM499.1","article-title":"Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture","volume":"7","author":"Crow","year":"2006","journal-title":"J. Hydrometeorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"W03423","DOI":"10.1029\/2007WR006357","article-title":"An adaptive ensemble Kalman filter for soil moisture data assimilation","volume":"44","author":"Reichle","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1175\/1520-0426(1995)012<0005:TOMATO>2.0.CO;2","article-title":"The Oklahoma Mesonet: A Technical Overview","volume":"12","author":"Brock","year":"1995","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1175\/JTECH1976.1","article-title":"Statewide Monitoring of the Mesoscale Environment: A Technical Update on the Oklahoma Mesonet","volume":"24","author":"McPherson","year":"2007","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1175\/2007JTECHA993.1","article-title":"Mesoscale Monitoring of Soil Moisture across a Statewide Network","volume":"25","author":"Illston","year":"2008","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"913","DOI":"10.3390\/s100100913","article-title":"Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method","volume":"10","author":"Lakhankar","year":"2010","journal-title":"Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jhydrol.2014.02.027","article-title":"Evaluation of multi-model simulated soil moisture in NLDAS-2","volume":"512","author":"Xia","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_29","first-page":"D03109","article-title":"Continental-scale Water and Energy Flux Analysis and Validation for the North American Land Data Assimilation System Project Phase 2 (NLDAS-2): Intercomparison and Application of Model Products","volume":"117","author":"Xia","year":"2012","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_30","unstructured":"(2020, April 20). GES DISC Dataset: NLDAS Noah Land Surface Model L4 Monthly Climatology 0.125 \u00d7 0.125 Degree V002 (NLDAS_NOAH0125_MC 002), Available online: https:\/\/disc.gsfc.nasa.gov\/datasets\/NLDAS_NOAH0125_MC_002\/summary."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","article-title":"The Soil Moisture Active Passive (SMAP) Mission","volume":"98","author":"Entekhabi","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_32","unstructured":"ONeill, P.E., Chan, S., Njoku, E.G., Jackson, T., and Bindlish, R. (2020, April 20). SMAP Enhanced L3 Radiometer Global Daily 9 Km EASE-Grid Soil Moisture, Version 3 2019. Available online: https:\/\/nsidc.org\/sites\/default\/files\/spl3smp_e-v004-userguide.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7755","DOI":"10.1029\/97JC03180","article-title":"Toward the true near-surface wind speed: Error modeling and calibration using triple collocation","volume":"103","author":"Stoffelen","year":"1998","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_34","unstructured":"Reichle, R., De Lannoy, G., Koster, R., Crow, W., Kimball, J., and Liu, Q. (2020, April 20). SMAP L4 Global 3-Hourly 9 Km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data, Version 5 2020. Available online: https:\/\/nsidc.org\/sites\/default\/files\/multi_spl4smau-v005-userguide_1.pdf."},{"key":"ref_35","first-page":"711","article-title":"A Conterminous United States Multi-Layer Soil Characteristics Data Set for Regional Climate and Hydrology Modeling, Earth Interactions","volume":"2","author":"Miller","year":"1998","journal-title":"Web-Based Publ. Res"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1175\/1520-0477(1996)077<0293:AHPOUC>2.0.CO;2","article-title":"A Historical Perspective of U.S. Climate Divisions","volume":"77","author":"Guttman","year":"1996","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1002\/joc.1077","article-title":"Seasonal to Interannual Variations of Soil Moisture Measured in Oklahoma","volume":"24","author":"Illston","year":"2004","journal-title":"Int. J. Climatol. A J. R. Meteorol. Soc."},{"key":"ref_38","first-page":"200","article-title":"Recent Advances in (Soil Moisture) Triple Collocation Analysis","volume":"45","author":"Gruber","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.rse.2013.06.013","article-title":"Estimating root mean square errors in remotely sensed soil moisture over continental scale domains","volume":"137","author":"Draper","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5194\/hess-15-75-2011","article-title":"A dynamic approach for evaluating coarse scale satellite soil moisture products","volume":"15","author":"Loew","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1175\/JHM-D-13-0158.1","article-title":"Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis","volume":"15","author":"Yilmaz","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jhydrol.2018.04.039","article-title":"Cross-evaluation of ground-based, multi-satellite and reanalysis precipitation products: Applicability of the Triple Collocation method across Mainland China","volume":"562","author":"Li","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Scipal, K., Holmes, T., de Jeu, R., Naeimi, V., and Wagner, W. (2008). A possible solution for the problem of estimating the error structure of global soil moisture data sets. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL035599"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"69","DOI":"10.5194\/npg-19-69-2012","article-title":"Structural and statistical properties of the collocation technique for error characterization","volume":"19","author":"Zwieback","year":"2012","journal-title":"Nonlinear Process. Geophys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6229","DOI":"10.1002\/2014GL061322","article-title":"Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target","volume":"41","author":"McColl","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Su, Z., van der Velde, R., Wang, L., Xu, K., Wang, X., and Wen, J. (2016). Blending Satellite Observed, Model Simulated, and in Situ Measured Soil Moisture over Tibetan Plateau. Remote Sens., 8.","DOI":"10.3390\/rs8030268"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"717","DOI":"10.5194\/essd-11-717-2019","article-title":"Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology","volume":"11","author":"Gruber","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_48","first-page":"1835","article-title":"Blending Noah, SMOS and In-Situ Soil Moisture Using Multiple Weighting and Sampling Schemes","volume":"22","author":"Zhang","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"129112","DOI":"10.1016\/j.jhydrol.2023.129112","article-title":"Multi-model based soil moisture simulation approach under contrasting weather conditions","volume":"617","author":"Shin","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_50","first-page":"6","article-title":"Hydrology and Earth System","volume":"27","author":"Madelon","year":"2023","journal-title":"Sciences"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"119056","DOI":"10.1016\/j.eswa.2022.119056","article-title":"Multiscale extrapolative learning algorithm for predictive soil moisture modeling & applications","volume":"213","author":"Chakraborty","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1071\/WF19193","article-title":"Soil moisture as an indicator of growing-season herbaceous fuel moisture and curing rate in grasslands","volume":"30","author":"Sharma","year":"2021","journal-title":"Int. J. Wildland Fire"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ramsauer, T., Wei\u00df, T., L\u00f6w, A., and Marzahn, P. (2021). RADOLAN_API: An Hourly Soil Moisture Data Set Based on Weather Radar, Soil Properties and Reanalysis Temperature Data. Remote Sens., 13.","DOI":"10.3390\/rs13091712"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2585","DOI":"10.1175\/JTECH-D-13-00084.1","article-title":"New Soil Property Database Improves Oklahoma Mesonet Soil Moisture Estimates","volume":"30","author":"Scott","year":"2013","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Das, N.N., Entekhabi, D., Kim, S., Jagdhuber, T., Dunbar, S., Yuehl, S., O\u2019Neill, P.E., Colliander, A., Walker, J., and Jackson, T.J. (2018, January 22\u201327). High Resolution Soil Moisture Product Based on Smap Active-Passive Approach Using Copernicus Sentinel 1 Data. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518932"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1175\/2010JHM1223.1","article-title":"Performance Metrics for Soil Moisture Retrievals and Application Requirements","volume":"11","author":"Entekhabi","year":"2010","journal-title":"J. Hydrometeorol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4880","DOI":"10.3390\/rs70404880","article-title":"Assessing the Impacts of Urbanization-Associated Land Use\/Cover Change on Land Surface Temperature and Surface Moisture: A Case Study in the Midwestern United States","volume":"7","author":"Jiang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e2020GL087648","DOI":"10.1029\/2020GL087648","article-title":"Marshland Loss Warms Local Land Surface Temperature in China","volume":"47","author":"Shen","year":"2020","journal-title":"Geophys. Res. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3450\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:08:33Z","timestamp":1760126913000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,7]]},"references-count":58,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133450"],"URL":"https:\/\/doi.org\/10.3390\/rs15133450","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,7,7]]}}}